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Single Image Super-Resolution via a Holistic Attention Network
[article]
2020
arXiv
pre-print
Informative features play a crucial role in the single image super-resolution task. Channel attention has been demonstrated to be effective for preserving information-rich features in each layer. However, channel attention treats each convolution layer as a separate process that misses the correlation among different layers. To address this problem, we propose a new holistic attention network (HAN), which consists of a layer attention module (LAM) and a channel-spatial attention module (CSAM),
arXiv:2008.08767v1
fatcat:nbtcquoy5vd5ligvpur6y4dqzi